The evolution of social and economic structures has worked hand-in-hand with the development of manufacturing technologies. While the first industrial revolution, or Industry 1.0, as we now refer to it, witnessed steam and hydropower replacing manual labor the second revolution saw assembly-line mass production of goods through the use of electricity. Then came the digital revolution, in which computer software and hardware were used for unprecedented automation.
Industry 4.0 definition
Today, we are well into the fourth industrial revolution, also known as Industry 4.0.
At the core of Industry 4.0 is connectivity— a shift from isolated, automated systems toward smart factories with an interconnected network of machines, materials, and products. It’s also a deliberate shift from the idea of just automating a few aspects to all the participants in the manufacturing framework and building capabilities in them to share information with each other.
The intelligent systems that are part of Industry 4.0 are called “cyber-physical” systems. Individually, these automated systems are self-contained and can bring huge efficiencies. But, with the advantage of their connectedness, they help reduce human intervention, optimize overall production, increase speed-to-market, and make better decisions.
Industry 4.0 technologies impact industries basics
Rapid growth in digital technologies has set the industrial revolution in motion, translating the concept into reality. These technologies have been the key enablers for the manufacturing, construction, and other industries to achieve integration, data sharing, rapid execution, and competitiveness.
The nine technology pillars, which serve as the building blocks of Industry 4.0, are:
- The industrial internet of things (IoT)
- Big data and analytics
- Simulation
- Horizontal and vertical integration
- The cloud
- Autonomous robots
- Augmented reality (AR)
- Cybersecurity
- Additive manufacturing or 3D printing
The exciting range of technologies available can make it difficult for organizations to know exactly where to begin. It can take time and resources to implement each of these, and it’s useful to evaluate which ones have the maximum potential to make a difference to your business cases.
Let’s look at three driving forces in particular that have the ability to create a strong business impact for most organizations.
Internet of Things (IoT)
IoT is the technology that allows devices to communicate with each other and share data. In the context of Industry 4.0, it’s also referred to as IIoT. Smart sensors enabled by IoT are attached to devices. These sensors collect data about the devices and upload it to the cloud, where all the information can be pooled together to make timely, intelligent decisions.
The IoT sensors are able to gather data using sound frequencies, vibrations, temperature and pressure changes, etc. Since data gathering is a prerequisite to almost everything else that happens as part of Industry 4.0, IoT is one of the most fundamental technologies.
IoT has numerous applications across industries. Specifically, it’s changing the way manufacturing works. In manufacturing operations, data collected by sensors give an opportunity to minimize waste. For example, by having a better sense of material movement through the floor, factories can do a better job of predicting the required inventory levels.
During equipment downtime, for instance, IoT sensors can automatically adjust HVAC equipment to preset configurations that can minimize energy consumption. IoT also facilitates predictive asset maintenance — a process that involves early detection of issues by looking for suboptimal performance and deviations from normal behavior. While this helps keep the assets healthy, it’s also beneficial in providing the workers a safe environment to work in.
Big Data and Analytics
As IoT sensors have gotten cheaper, it has become much easier to collect huge volumes of data. This means that other than using the data collected to make decisions on the fly, we can also use it to analyze patterns and interdependencies, simulate unknown scenarios, and predict the future.
In supply chains, data analytics can reveal unexpected insights within a factory and also help managers understand how the various participants in the complete chain interact with each other. Without IoT and cloud, this would be impossible to achieve as it requires many players such as producers, customers, and suppliers to collaborate with each other and share information.
Bayer Biopharmaceutical, Italy, for instance, used data analytics to achieve a 25% drop in maintenance costs and a 30-40% increase in operational efficiency.
Utility companies use analytics to anticipate the specific months or weeks that have overloads to prepare for more capacity during that time period. Fleet management companies use analytics to maintain their vehicles well, minimize their downtime, optimize travel routes to save costs, and deliver more flexible options for customers.
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial intelligence and machine learning are considered to be part of the IIoT pillar. The objective of AI is to create machines that can simulate human behavior. Machine learning is a subset of AI. Unlike traditional computing systems, whose algorithms are based on pre-programmed, rule-based actions (if X, do Y), machine learning systems have the ability to learn from scenarios. They start from scratch by understanding the rules based on the variables, constraints, and outcomes involved in a scenario. As more data is fed into a machine learning system, it gets better at predicting outcomes (similar to how humans learn with experience).
As a recent BCG report underscores, AI will be an integral part of the factory of the future. Example applications include:
- Automated guided vehicles carrying parts across the production floor and adjusting their routes based on obstacles
- Machines self-optimizing their parameters (example, a steel furnace self-adjusting its settings)
- Fine-tuning demand forecasting models
- Image recognition systems identifying defects quickly
Predictive maintenance in Industry 4.0
Asset management has a major role to play in Industry 4.0. In the field of asset maintenance, in particular, this has enabled managers to move from reactive (responding when equipment failures occur) and preventive (monitoring at regular intervals) to more intelligent, predictive maintenance.
Predictive algorithms collect asset intelligence using IoT sensors. They then use analytics to overlay that information with historical data as well as other external data sources (available in the cloud) to benchmark against performance indicators. This allows them to send alerts proactively before issues occur. Assets equipped with machine learning algorithms can compute possible outcomes for future scenarios.
Better asset health management has many benefits.
Intervening quickly means saving a lot of time spent troubleshooting maintenance issues. It also leads to higher equipment uptimes, which results in better utilization of assets and better use of the overall production capacity. In essence, the ability to manage assets efficiently results in reduced maintenance costs and an impressive ROI.
Bosch’s factory in Wuxi, China, is a success story. Installing IoT sensors and enabling data connectivity allowed them to predict machine interruptions and be more agile with changeovers and machine breakdowns. In September 2018, the world economic forum named it as a “lighthouse” factory, one of the nine advanced smart factories in the world.
Asset management becomes truly successful when it’s directly linked with a company’s overall strategy. For example, if a construction company’s mission is to be environmentally friendly, assets can be continuously monitored for parameters such as temperature, pressure, emissions, humidity, etc. and be adjusted to minimize their carbon footprint. A fleet management company, however, may choose to prioritize long-term utility of their trucks, so the systems will then optimize usage and monitor asset health through predictive maintenance.
Impact of not focusing on Industry 4.0
A study published by Intel, “Accelerate Industrial”, showed initial skepticism about the need to take part in the fourth industrial revolution has been replaced by the fear of missing out. With many examples across the world showcasing incredible inefficiencies by adopting Industry 4.0 technologies, manufacturing companies have started to understand that there will be a huge opportunity cost if they don’t begin their efforts to get on board now.
The pace of technology can be overwhelming for organizations who need time to adopt them into their processes. The best way to approach this is to start with one function at a time, such as asset management, and then persevere in the execution of a long-term strategic plan.
One of the best ways to begin sketching out your next strategic plan is start with data insights. ManagerPlus Lightning is a powerful enterprise asset management software that produces custom data reports, allowing you to make more precise decisions. Its visual dashboard displays exactly the data points you need when you need them. Before investing in an ambitious Industry 4.0 initiative, be sure you’re equipped with the metrics you need to make comparative measures.
Ready to take the first step toward better business intelligence that will power your Industry 4.0 strategy? Request a ManagerPlus Lightning demo!
